L05 Annotation & Positioning

Data Visualization (STAT 302)

Author

INSTRUCTIONS

Overview

The goal of this lab is to explore methods for annotating and positioning with ggplot2 plots. This lab also utilizes scale_* to a greater degree which is part of our next reading. In fact, students may find going through/reading chapter 11 Colour scales and legends useful.

Datasets

We’ll be using the blue_jays.rda, titanic.rda, Aus_athletes.rda, and tech_stocks.rda datasets.

Exercise 1

Using the blue_jays.rda dataset, recreate the following graphic as precisely as possible.

Hints:

  • Transparency is 0.8
  • Point size 2
  • Create a label_info dataset that is a subset of original data, just with the 2 birds to be labeled
  • Shift label text horizontally by 0.5
  • See ggplot2 textbook 8.3 building custom annotations
  • Annotation size is 4
  • Classic theme

Exercise 2

Using the tech_stocks dataset, recreate the following graphics as precisely as possible. Use the column price_indexed.

Plot 1

Hints:

  • Create a label_info dataset that is a subset of original data, just containing the last day’s information for each of the 4 stocks
  • serif font
  • Annotation size is 4

Plot 2

Hints:

  • Package ggrepel
    • box.padding is 0.6
    • Minimum segment length is 0
    • Horizontal justification is to the right
    • seed of 9876
  • Annotation size is 4
  • serif font


Exercise 3

Using the titanic.rda dataset, recreate the following graphic as precisely as possible.

Hints:

  • Create a new variable that uses died and survived as levels/categories
  • Hex colors: #D55E00D0, #0072B2D0 (no alpha is being used)


Exercise 4

Use the athletes_dat dataset — extracted from Aus_althetes.rda — to recreate the following graphic as precisely as possible. Create the graphic twice: once using patchwork and once using cowplot.

Code 
# Get list of sports played by BOTH sexes
both_sports <- Aus_athletes %>%
  # dataset of columns sex and sport 
  # only unique observations
  distinct(sex, sport) %>%
  # see if sport is played by one gender or both
  count(sport) %>%
  # only want sports played by BOTH sexes
  filter(n == 2) %>%
  # get list of sports
  pull(sport)

# Process data
athletes_dat <- Aus_athletes %>%
  # only keep sports played by BOTH sexes
  filter(sport %in% both_sports) %>%
  # rename track (400m) and track (sprint) to be track
  # case_when will be very useful with shiny apps
  mutate(
    sport = case_when(
      sport == "track (400m)" ~ "track",
      sport == "track (sprint)" ~ "track",
      TRUE ~ sport
      )
    )

Hints:

  • Build each plot separately
  • Bar plot: lower limit 0, upper limit 95
  • Bar plot: shift bar labels by 5 units and top justify
  • Bar plot: label size is 5
  • Bar plot: #D55E00D0 & #0072B2D0 — no alpha
  • Scatterplot: #D55E00D0 & #0072B2D0 — no alpha
  • Scatterplot: filled circle with “white” outline; size is 3
  • Scatterplot: rcc is red blood cell count; wcc is white blood cell count
  • Boxplot: outline #D55E00 and #0072B2; shading #D55E0040 and #0072B240
  • Boxplot: should be made narrower; 0.5
  • Boxplot: Legend is in top-right corner of bottom plot
  • Boxplot: Space out labels c("female ", "male")
  • Boxplot: Legend shading matches hex values for top two plots

Exercise 5

Create the following graphic using patchwork.

Hints:

  • Use plots created in exercise 4
  • inset theme is classic
    • Useful values: 0, 0.45, 0.75, 1
  • plot annotation "A"